PSO-GA based hybrid with Adam Optimization for ANN training with application in Medical Diagnosis

Yadav, Rajesh Kumar; Anubhav,

doi:10.1016/j.cogsys.2020.08.011

Cited by 61 publications

(25 citation statements)

References 19 publications

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“…Among them, c , r , v ( t ), and x ( t ) represent the learning factor, random number of [0,1] interval, particle velocity, and particle position respectively; w ( t ), t , and T max represent inertia weight, number of iterations, and maximum number of iterations respectively, and the value of learning factor is 2 [ 26 , 27 ]. The update of velocity is constrained by the limited value of particle velocity and calculated according to the following formulae:

…”

Section: Information Security Risk Assessment Based On Bp Neural Network Optimized By Pso Algorithmmentioning

confidence: 99%

[Retracted] Information System Security Evaluation Algorithm Based on PSO‐BP Neural Network

Zheng

2021

Computational Intelligence and Neuroscience

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With the deepening of big data and the development of information technology, the country, enterprises, organizations, and even individuals are more and more dependent on the information system. In recent years, all kinds of network attacks emerge in an endless stream, and the losses are immeasurable. Therefore, the protection of information system security is a problem that needs to be paid attention to in the new situation. The existing BP neural network algorithm is improved as the core algorithm of the security intelligent evaluation of the rating information system. The input nodes are optimized. In the risk factor identification stage, most redundant information is filtered out and the core factors are extracted. In the risk establishment stage, the particle swarm optimization algorithm is used to optimize the initial network parameters of BP neural network algorithm to overcome the dependence of the network on the initial threshold, At the same time, the performance of the improved algorithm is verified by simulation experiments. The experimental results show that compared with the traditional BP algorithm, PSO-BP algorithm has faster convergence speed and higher accuracy in risk value prediction. The error value of PSO-BP evaluation method is almost zero, and there is no error fluctuation in 100 sample tests. The maximum error value is only 0.34 and the average error value is 0.21, which proves that PSO-BP algorithm has excellent performance.

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…”

Section: Information Security Risk Assessment Based On Bp Neural Network Optimized By Pso Algorithmmentioning

confidence: 99%

[Retracted] Information System Security Evaluation Algorithm Based on PSO‐BP Neural Network

Zheng

2021

Computational Intelligence and Neuroscience

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“…A DFFNN is a type of ANN that provides a powerful solution when trying to create an approximate model of a dataset capable of predicting an output given several inputs. Although a similar approximation can be made with a linear regression system, there are several constraints that the data needs to comply with to have a small RMSE [ 29 ]. The ANN can solve complex problems in various fields such as solving function approximations and generation of meaningful patterns [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

Sensor Data Fusion for a Mobile Robot Using Neural Networks

Barreto-Cubero

Gómez-Espinosa

Cabello

et al. 2021

Sensors

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Mobile robots must be capable to obtain an accurate map of their surroundings to move within it. To detect different materials that might be undetectable to one sensor but not others it is necessary to construct at least a two-sensor fusion scheme. With this, it is possible to generate a 2D occupancy map in which glass obstacles are identified. An artificial neural network is used to fuse data from a tri-sensor (RealSense Stereo camera, 2D 360° LiDAR, and Ultrasonic Sensors) setup capable of detecting glass and other materials typically found in indoor environments that may or may not be visible to traditional 2D LiDAR sensors, hence the expression improved LiDAR. A preprocessing scheme is implemented to filter all the outliers, project a 3D pointcloud to a 2D plane and adjust distance data. With a Neural Network as a data fusion algorithm, we integrate all the information into a single, more accurate distance-to-obstacle reading to finally generate a 2D Occupancy Grid Map (OGM) that considers all sensors information. The Robotis Turtlebot3 Waffle Pi robot is used as the experimental platform to conduct experiments given the different fusion strategies. Test results show that with such a fusion algorithm, it is possible to detect glass and other obstacles with an estimated root-mean-square error (RMSE) of 3 cm with multiple fusion strategies.

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“…With the improvement of the mathematical theory, this problem can be solved using optimization technology. As the particle swarm optimization (PSO) algorithm is a good global optimization method using in the different optimization problems (Moradi et al, 2020;Song et al, 2021;Yadav & Anubhav, 2020;Zhang et al, 2021), a PSO algorithm is employed in this study to find the optimal parameters for different surrogate models with the k-fold cross-validation method (Zhou et al, 2017). The optimization step can be summarized as follows:…”

Section: Establishment Of the Surrogate Modelsmentioning

confidence: 99%